class_docstr: Featurize messages using SpaCy.
docstr: Feature vector for a single document / sentence / tokens.
docstr_tok: ["Feature","vector","for","a","single","document","\/","sentence","\/","tokens","."]
code: def _features_for_doc(self, doc: "Doc") -> np.ndarray:
      """Feature vector for a single document / sentence / tokens."""
      return np.array(
          [t.vector for t in doc if t.text and t.text.strip()]
      )
  
imports: import numpy
  import typing
  import logging
  from typing import Any, Text, Dict, List, Type
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.featurizers.dense_featurizer.dense_featurizer import DenseFeaturizer
  from rasa.nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.constants import SPACY_DOCS, DENSE_FEATURIZABLE_ATTRIBUTES, FEATURIZER_CLASS_ALIAS
  from rasa.shared.nlu.constants import TEXT, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE
  from rasa.utils.tensorflow.constants import POOLING, MEAN_POOLING
label_desc: Use image node_id 6 for calling the SpacyFeaturizer obj's underlying member method code with example usage: obj._features_for_doc(doc) and returns: np